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Off-policy evaluation

Off-policy Evaluation (OPE), or offline evaluation in general, evaluates the performance of hypothetical policies leveraging only offline log data. It is particularly useful in applications where the online interaction involves high stakes and expensive setting such as precision medicine and recommender systems.

Papers

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Showing 1–50 of 265 papers

TitleStatusHype
Doubly Robust Off-Policy Evaluation for Ranking Policies under the Cascade Behavior ModelCode2
Off-Policy Evaluation for Large Action Spaces via EmbeddingsCode2
COptiDICE: Offline Constrained Reinforcement Learning via Stationary Distribution Correction EstimationCode1
Counterfactual Evaluation of Slate Recommendations with Sequential Reward InteractionsCode1
Anytime-valid off-policy inference for contextual banditsCode1
BCORLE(): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce MarketCode1
Trajectory World Models for Heterogeneous EnvironmentsCode1
Model Selection for Offline Reinforcement Learning: Practical Considerations for Healthcare SettingsCode1
Evaluating the Robustness of Off-Policy EvaluationCode1
Optimal Off-Policy Evaluation from Multiple Logging PoliciesCode1
Benchmarks for Deep Off-Policy EvaluationCode1
A Policy-Guided Imitation Approach for Offline Reinforcement LearningCode1
Off-Policy Evaluation of Ranking Policies under Diverse User BehaviorCode1
A Deep Reinforcement Learning Approach to Marginalized Importance Sampling with the Successor RepresentationCode1
Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy EvaluationCode1
Offline RL Without Off-Policy EvaluationCode1
Off-Policy Evaluation via Adaptive Weighting with Data from Contextual BanditsCode1
Towards Assessing and Benchmarking Risk-Return Tradeoff of Off-Policy EvaluationCode1
Active Offline Policy SelectionCode1
Doubly Robust Distributionally Robust Off-Policy Evaluation and LearningCode1
Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy EvaluationCode1
SCOPE-RL: A Python Library for Offline Reinforcement Learning and Off-Policy EvaluationCode1
Hindsight-DICE: Stable Credit Assignment for Deep Reinforcement LearningCode0
Harnessing Distribution Ratio Estimators for Learning Agents with Quality and DiversityCode0
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy EvaluationCode0
Adaptive Estimator Selection for Off-Policy EvaluationCode0
Balanced off-policy evaluation in general action spacesCode0
Batch Stationary Distribution EstimationCode0
Future-Dependent Value-Based Off-Policy Evaluation in POMDPsCode0
Off-policy Evaluation with Deeply-abstracted StatesCode0
Conformal Off-policy PredictionCode0
From Importance Sampling to Doubly Robust Policy GradientCode0
Hallucinated Adversarial Control for Conservative Offline Policy EvaluationCode0
Importance Sampling Policy Evaluation with an Estimated Behavior PolicyCode0
Doubly Robust Kernel Statistics for Testing Distributional Treatment EffectsCode0
Double Reinforcement Learning for Efficient Off-Policy Evaluation in Markov Decision ProcessesCode0
A Minimax Learning Approach to Off-Policy Evaluation in Confounded Partially Observable Markov Decision ProcessesCode0
Deep Proxy Causal Learning and its Application to Confounded Bandit Policy EvaluationCode0
Distributional Off-policy Evaluation with Bellman Residual MinimizationCode0
Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment SettingsCode0
Deeply-Debiased Off-Policy Interval EstimationCode0
Distributional Off-Policy Evaluation for Slate RecommendationsCode0
DOLCE: Decomposing Off-Policy Evaluation/Learning into Lagged and Current EffectsCode0
Doubly robust off-policy evaluation with shrinkageCode0
Confident Off-Policy Evaluation and Selection through Self-Normalized Importance WeightingCode0
Doubly Robust Estimator for Off-Policy Evaluation with Large Action SpacesCode0
Counterfactual Mean EmbeddingsCode0
A Multi-Agent Reinforcement Learning Framework for Off-Policy Evaluation in Two-sided MarketsCode0
Balanced Off-Policy Evaluation for Personalized PricingCode0
Causal Deepsets for Off-policy Evaluation under Spatial or Spatio-temporal InterferencesCode0
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